Clarify the NVidia driver version requirements.

README.md

@@ -425,24 +425,25 @@ with CUDA 11.1 or newer, and CuDNN 8.0.5 or newer. Other combinations of
 operating system, CUDA, and CuDNN are possible, but require building from
 source.
 
-* CUDA 11.1 or newer is required.
+* CUDA 11.1 or newer is *required*.
   * You may be able to use older CUDA versions if you build from source, but
-    there are known bugs in CUDA in all CUDA versions older than 11.1.
+    there are known bugs in CUDA in all CUDA versions older than 11.1, so we
+    do not ship prebuilt binaries for older CUDA versions.
 * The supported cuDNN versions for the prebuilt wheels are:
   * cuDNN 8.2 or newer. We recommend using the cuDNN 8.2 wheel if your cuDNN
     installation is new enough, since it supports additional functionality.
   * cuDNN 8.0.5 or newer.
-* To use the prebuilt wheels, we recommend you have NVidia driver 470.82.01 or
-  newer installed.
-  * If your GPU has a
-    [CUDA compute capability](https://developer.nvidia.com/cuda-gpus) that
-    matches one of the architectures that the JAX wheels are built for
-    (3.5, 5.2, 6.0, 7.0, and 8.0), you may also be able to use older drivers
-    that are version 450.80.02 or newer.
-  * If your GPU does not match one of those architectures, you must either
-    use driver 470.82.01 or newer, use the
-    [CUDA forward compatibility packages](https://docs.nvidia.com/deploy/cuda-compatibility/),
-    or build jaxlib from source.
+* You *must* use an NVidia driver version that is at least as new as your
+  [CUDA toolkit's corresponding driver version](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions).
+  For example, if you have CUDA 11.4 update 4 installed, you must use NVidia
+  driver 470.82.01 or newer if on Linux. This is a strict requirement that
+  exists because JAX relies on JIT-compiling code; older drivers may lead to
+  failures.
+  * If you need to use an newer CUDA toolkit with an older driver, for example
+    on a cluster where you cannot update the NVidia driver easily, you may be
+    able to use the
+    [CUDA forward compatibility packages](https://docs.nvidia.com/deploy/cuda-compatibility/)
+    that NVidia provides for this purpose.
 
 
 Next, run
@@ -461,7 +462,7 @@ version for jaxlib explicitly:
 ```bash
 pip install --upgrade pip
 
-# Installs the wheel compatible with Cuda >= 11.1 and cudnn >= 8.2
+# Installs the wheel compatible with Cuda >= 11.4 and cudnn >= 8.2
 pip install jax[cuda11_cudnn82] -f https://storage.googleapis.com/jax-releases/jax_releases.html
 
 # Installs the wheel compatible with Cuda >= 11.1 and cudnn >= 8.0.5